Plasmid-borne carbapenem resistance has become a serious global threat in hospital settings. As hospitals face a dwindling number of treatment options, there is a great urgency to track the movement of carbapenem-resistant plasmids and the associated resistant genes. While whole genome sequencing has great diagnostic potential, plasmids pose a unique set of challenges as they lack a clear, well-defined structure and contain a wide range of repetitive genetic elements. Additionally, plasmids routinely undergo complex rearrangements during patient colonization, driven by environmental pressures such as continual exposure to antibiotics. Finally, plasmid movements through horizontal gene transfer frequently results in further diversification, predominantly from host-specific selection pressures. As a result of these challenges, tracking the movement of plasmids through whole genome sequencing often requires painstaking manual bioinformatics approaches. In this proposal, with an initial focus on Enterobacteriaceae plasmids that carry the KPC gene, we look to develop an automated diagnostics platform that will allow hospitals to closely monitor drug-resistant plasmids. Our platform will provide infection control units a searchable cloud-based database of all their drug- resistant plasmids, their association with various strains, and the various points of entry within the hospital. Our approach uses two different alignment methodologies for plasmid identification: one methodology is designed to rapidly identify the generic content of the plasmid; the other methodology is designed to uniquely quantify the transposon regions around resistance genes. Our proposal aims are: 1) Develop a plasmid framework that quantifies plasmids with both global and transposon biomarker sequences; 2) Develop a plasmid-strain alignment framework that will search new strains for plasmid biomarker sequences and then identify other strains that contain similar global biomarker sequences; 3) Develop a transposon-alignment framework that will search new strains for KPC-gene carrying transposon sequences and then identify other strains with similar biomarker sequences. Our approach was tested against two closely-related plasmids at a single hospital. By first establishing key biomarkers, we were able to rapidly identify over 240 strains from NCBI that had inherited the same plasmids over a four-year period and across three species. Our algorithm was also able to identify a key HGT event for one of the plasmids, which was independently documented in a published study. Our cloud-based plasmid diagnostics framework will be implemented by processing over 4,000 Enterobacteriaceae strains from NCBI. Validations will be made using manual bioinformatics protocols.